Is the emergency department used as a substitute or a complement to primary care in Medicaid?

Policies to decrease low-acuity emergency department (ED) use have traditionally assumed that EDs are a substitute for unavailable primary care (PC). However, such policies can exacerbate ED overcrowding, rather than ameliorate it, if patients use EDs to complement, rather than substitute, their PC use. We tested whether Medicaid managed care enrolees visit the ED for nonemergent and PC treatable conditions to substitute for or to complement PC. Based on consumer choice theory, we modelled county-level monthly ED visit rate as a function of PC supply and used 2012-2015 New York Statewide Planning and Research Cooperative System (SPARCS) outpatient data and non-linear least squares method to test substitution vs complementarity. In the post-Medicaid expansion period (2014-2015), ED and PC are substitutes state-wide, but are complements in highly urban and poorer counties during nights and weekends. There is no evidence of complementarity before the expansion (2012-2013). Analyses by PC provider demonstrate that the relationship between ED and PC differs depending on whether PC is provided by physicians or advanced practice providers. Policies to reduce low-acuity ED use via improved PC access in Medicaid are likely to be most effective if they focus on increasing actual appointment availability, ideally by physicians, in areas with low PC provider supply. Different aspects of PC access may be differently related to low-acuity ED use.

Preferences for risks and benefits of treatment outcomes for chronic low back pain: Choice-based conjoint measure development and discrete choice experiment.

INTRODUCTION: Understanding individual patient preferences for chronic low back pain (cLBP) outcomes is essential for targeting available therapeutic options; yet tools to elicit patient outcome preferences are limited. OBJECTIVE: To develop and test a choice-based conjoint (CBC) measure, commonly used in behavioral economics research, to elicit what outcomes patients with cLBP want to achieve and avoid. DESIGN: We developed a survey-based CBC measure to allow patients to make risk/benefit trade-off choices between possible treatment outcomes. After extensive literature, clinician, and patient input, our measure included seven attributes: fatigue, anxiety/depression, difficulty thinking/making decisions, pain intensity, physical abilities, change in pain, and ability to enjoy life despite pain. Random-parameters logit models were used to estimate strength of preferences, and latent class analysis was used to identify patient characteristics associated with distinct preference. SETTING: Online study using the Sawtooth web-based platform. PARTICIPANTS: Two hundred eleven individuals with cLBP recruited from online advertising as well as at clinical sites across multiple academic and private institutions. INTERVENTIONS: Not applicable. RESULTS: The most valued outcome was the highest level of physical activity (ß = 1.6-1.98; p < .001), followed by avoiding cognitive difficulties (ß = -1.48; p < .001). Avoidance of severe pain was comparable to avoiding constant fatigue and near-constant depression/anxiety (ß = -0.99, -1.02); p < .001). There was an association between preferences and current pain/disability status; patients with higher pain had a stronger preference to avoid severe pain, whereas those with higher disability have stronger preferences for achieving physical activity. The latent class analysis identified two distinct groups: (1) more risk-seeking and willing to accept worse outcomes (56%); and (2) more risk-averse with a stronger preference for achieving maximum benefits (44%). CONCLUSIONS: Our study illuminated cLBP patient preferences for treatment outcomes and heterogeneity in these preferences. Patients stressed the importance of reaching high physical activity and avoiding cognitive declines, even over a desire to avoid pain. More work is needed to understand patient preferences to aid informed, shared decisions.

CAPER: patient preferences to inform nonsurgical treatment of chronic low back pain: a discrete-choice experiment.

OBJECTIVE: We developed and used a discrete-choice measure to study patient preferences with regard to the risks and benefits of nonsurgical treatments when they are making treatment selections for chronic low back pain. METHODS: "CAPER TREATMENT" (Leslie Wilson) was developed with standard choice-based conjoint procedures (discrete-choice methodology that mimics an individual's decision-making process). After expert input and pilot testing, our final measure had 7 attributes (chance of pain relief, duration of relief, physical activity changes, treatment method, treatment type, treatment time burden, and risks of treatment) with 3-4 levels each. Using Sawtooth software (Sawtooth Software, Inc., Provo, UT, USA), we created a random, full-profile, balanced-overlap experimental design. Respondents (n = 211) were recruited via an emailed online link and completed 14 choice-based conjoint choice pairs; 2 fixed questions; and demographic, clinical, and quality-of-life questions. Analysis was performed with random-parameters multinomial logit with 1000 Halton draws. RESULTS: Patients cared most about the chance of pain relief, followed closely by improving physical activity, even more than duration of pain relief. There was comparatively less concern about time commitment and risks. Gender and socioeconomic status influenced preferences, especially with relation to strength of expectations for outcomes. Patients experiencing a low level of pain (Pain, Enjoyment, and General Activity Scale [PEG], question 1, numeric rating scale score<4) had a stronger desire for maximally improved physical activity, whereas those in a high level of pain (PEG, question 1, numeric rating scale score>6) preferred both maximum and more limited activity. Highly disabled patients (Oswestry Disability Index score>40) demonstrated distinctly different preferences, placing more weight on achieving pain control and less on improving physical activity. CONCLUSIONS: Individuals with chronic low back pain were willing to trade risks and inconveniences for better pain control and physical activity. Additionally, different preference phenotypes exist, which suggests a need for clinicians to target treatments to particular patients.

Coroner county systems are associated with a higher likelihood of unclassified drug overdoses compared to medical examiner county systems.

Background: There is a striking geographic variation in drug overdose deaths without a specific drug recorded, many of which likely involve opioids. Knowledge of the reasons underlying this variation is limited.Objectives: We sought to understand the role of medicolegal death investigation (MDI) systems in unclassified drug overdose mortality.Methods: This is an observational study of 2014 and 2018 fatal drug overdoses and U.S. county-level MDI system type (coroner vs medical examiner). Mortality data are from the CDC's National Center for Health Statistics. We estimated multivariable logistic regressions to quantify associations between MDI system type and several outcome variables: whether the drug overdose was unclassified and whether involvement of any opioid, synthetic opioid, methadone, and heroin was recorded (vs unclassified), for 2014 (N = 46,996) and 2018 (N = 67,359).Results: In 2018, drug overdose deaths occurring in coroner counties were almost four times more likely to be unclassified (OR 3.87, 95% CI 2.32, 6.46) compared to medical examiner counties. These odds ratios are twice as large as in 2014 (difference statistically significant, P < .001), indicating that medical examiner counties are improving identification of opioids in drug overdoses faster than coroner counties.Conclusions: Accurate reporting of drug overdose deaths depends on MDI systems. When developing state policies and local interventions aimed to decrease opioid overdose mortality, decision-makers should understand the role their MDI system is playing in underestimating the extent of the opioid overdose crisis. Improvements to state and county MDI systems are desirable if accurate reporting and appropriate policy response are to be achieved.

Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative.

Importance: The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. Objectives: To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and Participants: In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Main Outcomes and Measures: Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. Results: The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance: This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.

Understanding how psychosocial factors relate to seeking medical care among older adults using a new model of care seeking.

RATIONALE: Research has identified psychosocial factors related to the use of health services among the older population; however, the specific roles by which these factors drive behavior have not been identified and empirically tested. OBJECTIVE: This study tested whether previously identified psychosocial factors decrease or increase the motivational potential to seek care, the motivational sensitivity to perceived access, or the motivational sensitivity to perceived need. METHODS: The 2014 U.S. Health and Retirement Study was used. Analysis was based on 2589 older noninstitutionalized respondents (age greater than 64). The dependent variable was the number of healthcare provider visits in the preceding two years. Psychosocial factors included were life satisfaction, social network indicators, optimism, pessimism, positive social support, hopelessness, loneliness, self-efficacy, health efficacy, positive affect, negative affect, and purpose in life. Covariates included age and sex. Maximum likelihood estimation of an interpretable structural model was used. RESULTS: Results of the study provide evidence that psychosocial variables are related to health care seeking through both motivational potential and sensitivity parameters. Some psychosocial variables are related to multiple roles. For example, pessimism is related to a lower motivational potential and is more sensitive to access at higher levels of access, whereas hopelessness is related to a higher motivational potential and more sensitive to need and access at lower levels of each. CONCLUSIONS: Findings imply psychosocial characteristics are related to health care seeking and utilization of older adults via different roles that can countervail each other, and therefore the influence of interventions can be complex. To address this, complex interventions may be required.

Fatal opioid overdoses in the U.S. declined more than reported between 2017 and 2018.

Background: In U.S. death records, many drug overdoses do not have classified drug involvement, which challenges surveillance of opioid overdoses across time and space.Objective: To estimate the 2017-2018 change in opioid overdose deaths that accounts for probable opioid involvement in unclassified drug overdose deaths.Methods: In this retrospective design study, data on all drug overdose decedents from 2017-2018 in the U.S. were used to calculate the year-to-year change in known opioid overdoses, predict opioid involvement in unclassified drug overdoses, and estimate the year-to-year change in corrected opioid overdoses, which include both known and predicted opioid deaths. We used the Multiple Cause of Death (MCOD) data from CDC.Results: We estimated that the decrease in the age-adjusted opioid overdose death rate from 2017-2018 was 7.0%. There is a striking variation across states. Age-adjusted opioid overdose death rates decreased by 9.9% in Ohio and more than 5.0% in other Appalachian states (Pennsylvania, West Virginia, Kentucky), while they increased by 6.8% in Delaware.Conclusions: Our models suggest that opioid overdose-related mortality declined from 2017 to 2018 at a higher rate than reported (7.0% versus than the reported 2.0%), potentially indicating that clinical efforts and federal, state, and local government policies designed to control the epidemic have been effective in most states. Our local area estimates can be used by researchers, policy-makers and public health officials to assess effectiveness of state policies and interventions in smaller jurisdictions implemented in response to the crisis.

Acute myocardial infarction associated with unconventional natural gas development: A natural experiment.

BACKGROUND: Whereas it is plausible that unconventional natural gas development (UNGD) may adversely affect cardiovascular health, little is currently known. We investigate whether UNGD is associated with acute myocardial infarction (AMI). METHODS: In this observational study leveraging the natural experiment generated by New York's ban on hydraulic fracturing, we analyzed the relationship between age- and sex-specific county-level AMI hospitalization and mortality rates and three UNGD drilling measures. This longitudinal panel analysis compares Pennsylvania and New York counties on the Marcellus Shale observed over 2005-2014 (N = 2840 county-year-quarters). RESULTS: A hundred cumulative wells is associated with 0.26 more hospitalizations per 10,000 males 45-54y.o. (95% CI 0.07,0.46), 0.40 more hospitalizations per 10,000 males 65-74y.o. (95% CI 0.09,0.71), 0.47 more hospitalizations per 10,000 females 65-74y.o. (95% CI 0.18,0.77) and 1.11 more hospitalizations per 10,000 females 75y.o.+ (95% CI 0.39,1.82), translating into 1.4-2.8% increases. One additional well per square mile is associated with 2.63 more hospitalizations per 10,000 males 45-54y.o. (95% CI 0.67,4.59) and 9.7 hospitalizations per 10,000 females 75y.o.+ (95% CI 1.92,17.42), 25.8% and 24.2% increases, respectively. As for mortality rates, a hundred cumulative wells is associated with an increase of 0.09 deaths per 10,000 males 45-54y.o. (95% CI 0.02,0.16), a 5.3% increase. CONCLUSIONS: Cumulative UNGD is associated with increased AMI hospitalization rates among middle-aged men, older men and older women as well as with increased AMI mortality among middle-aged men. Our findings lend support for increased awareness about cardiovascular risks of UNGD and scaled-up AMI prevention as well as suggest that bans on hydraulic fracturing can be protective for public health.

The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction.

Background: The majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy. Methods and Findings: In a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients. Conclusions: This is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

Natural gas development, flaring practices and paediatric asthma hospitalizations in Texas.

BACKGROUND: Recent advancements in drilling technology led to a rapid increase in natural gas development (NGD). Air pollution may be elevated in these areas and may vary by drilling type (conventional and unconventional), production volume and gas flaring. Impacts of NGD on paediatric asthma are largely unknown. This study quantifies associations between specific NGD activities and paediatric asthma hospitalizations in Texas. METHODS: We leveraged a database of Texas inpatient hospitalizations between 2000 and 2010 at the zip code level by quarter to examine associations between NGD and paediatric asthma hospitalizations, where our primary outcome is 0 vs ≥1 hospitalization. We used quarterly production reports to assess additional drilling-specific exposures at the zip code-level including drilling type, production and gas flaring. We developed logistic regression models to assess paediatric asthma hospitalizations by zip code-quarter-year observations, thus capturing spatiotemporal exposure patterns. RESULTS: We observed increased odds of ≥1 paediatric asthma hospitalization in a zip code per quarter associated with increasing tertiles of NGD exposure and show that spatiotemporal variation impacts results. Conventional drilling, compared with no drilling, is associated with odds ratios up to 1.23 [95% confidence interval (CI): 1.13, 1.34], whereas unconventional drilling is associated with odds ratios up to 1.59 (95% CI: 1.46, 1.73). Increasing production volumes are associated with increased paediatric asthma hospitalizations in an exposure-response relationship, whereas associations with flaring volumes are inconsistent. CONCLUSIONS: We found evidence of associations between paediatric asthma hospitalizations and NGD, regardless of drilling type. Practices related to production volume may be driving these positive associations.

Using contributing causes of death improves prediction of opioid involvement in unclassified drug overdoses in US death records.

BACKGROUND AND AIMS: A substantial share of fatal drug overdoses is missing information on specific drug involvement, leading to under-reporting of opioid-related death rates and a misrepresentation of the extent of the opioid epidemic. We aimed to compare methodological approaches to predicting opioid involvement in unclassified drug overdoses in US death records and to estimate the number of fatal opioid overdoses from 1999 to 2016 using the best-performing method. DESIGN: This was a secondary data analysis of the universe of drug overdoses in 1999-2016 obtained from the National Center for Health Statistics Detailed Multiple Cause of Death records. SETTING: United States. CASES: A total of 632 331 drug overdose decedents. Drug overdoses with known drug classification comprised 78.2% of the cases (n = 494 316) and unclassified drug overdoses (ICD-10 T50.9) comprised 21.8% (n = 138 015). MEASUREMENTS: Known opioid involvement was defined using ICD-10 codes T40.0-40.4 and T40.6, recorded in the set of contributing causes. Opioid involvement in unclassified drug overdoses was predicted using multiple methodological approaches: logistic regression and machine learning techniques, inclusion/exclusion of contributing causes of death and inclusion/exclusion of county-level characteristics. Having selected the model with the highest predictive ability, we calculated corrected estimates of opioid-related mortality. FINDINGS: Logistic regression and random forest models performed similarly. Including contributing causes substantially improved predictive accuracy, while including county characteristics did not. Using a superior prediction model, we found that 71.8% of unclassified drug overdoses in 1999-2016 involved opioids, translating into 99 160 additional opioid-related deaths, or approximately 28% more than reported. Importantly, there was a striking geographic variation in undercounting of opioid overdoses. CONCLUSIONS: In modeling opioid involvement in unclassified drug overdoses, highest predictive accuracy is achieved using a statistical model-either logistic regression or a random forest ensemble-with decedent characteristics and contributing causes of death as predictors.

Are Increasing Trends in Opioid-Related Hospitalizations Attributable to Increases in Diagnosis Recordability? Evidence from 2 Large States.

Based on calculations using all-listed diagnoses, the Agency for Healthcare Research and Quality (AHRQ) reports increasing national trends in opioid-related hospitalizations. It is unclear whether the reported increases are attributable to increases in available diagnosis fields. We leveraged increases in available diagnosis fields, ie, diagnosis recordability, in 2 states to examine their effects on opioid-related hospitalizations, graphically and with nonlinear least squares. Hospitalization data from Texas (1999-2011, N = 36 593 049) and New York (2005-2015Q3, N = 27 582 208) were aggregated to quarter-year in each state. Opioid-related hospitalizations were identified using the same International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) Diagnosis Codes as AHRQ. In Texas, the increase in diagnosis recordability resulted in a 29.9% discrete shift in the number of recorded opioid diagnoses and a 3-fold increase in the slope. In New York, a smaller discrete shift (3.1%) and a 3-fold increase in the slope were identified, although a more pronounced change in the trend occurred 5 years earlier (slope change from flat to increasing). Increases in recordability lead to a broader definition of opioid-related hospitalizations, if all-listed diagnoses are used; we found that more hospitalizations are identified using the postchange definition than with the prechange definition (9.7% more in Texas and 4.9% more in New York after 4 years). We conclude that reported increases in opioid-related hospitalizations are partially attributable to increases in diagnosis recordability. Cross-state and temporal comparisons of opioid-related hospitalization rates based on all-listed diagnoses can misrepresent the true relative extent of opioid-related hospital use and therefore of the opioid epidemic.

Unclassified drug overdose deaths in the opioid crisis: emerging patterns of inequity.

OBJECTIVE: Examine whether individual, geographic, and economic phenotypes predict missing data on specific drug involvement in overdose deaths, manifesting inequities in overdose mortality data, which is a key data source used in measuring the opioid epidemic. MATERIALS AND METHODS: We combined national data sources (mortality, demographic, economic, and geographic) from 2014-2016 in a multi-method analysis of missing drug classification in the overdose mortality records (as defined by the use of ICD-10 T50.9 on death certificates). We examined individual disparities in decedent-level multivariate logistic regression models, geographic disparities in spatial analysis (heat maps), and economic disparities in a combination of temporal trend analyses (descriptive statistics) and both decedent- and county-level multivariate logistic regression models. RESULTS: Our analyses consistently found higher rates of unclassified overdoses in decedents of female gender, White race, non-Hispanic ethnicity, with college education, aged 30-59 and those from poorer counties. Despite the fact that unclassified drug overdose death rates have reduced over time, gaps persist between the richest and poorest counties. There are also striking geographic differences both across and within states. DISCUSSION: Given the essential role of mortality data in measuring the scale of the opioid epidemic, it is important to understand the individual and community inequities underlying the missing data on specific drug involvements. Knowledge of these inequities could enhance our understanding of the opioid crisis and inform data-driven interventions and policies with more equitable resource allocations. CONCLUSION: Multiple individual, geographic, and economic disparities underlie unclassified overdose deaths, with important implications for public health informatics and addressing the opioid crisis.

Did Medicaid expansion matter in states with generous Medicaid?

OBJECTIVES: It is unclear whether the Medicaid expansion under the Affordable Care Act had an effect on coverage in states with relatively generous pre-expansion Medicaid eligibility levels. We examined the effect of the Medicaid expansions on Medicaid coverage in 4 generous states: New York, Vermont, Massachusetts, and Delaware. STUDY DESIGN: We used the American Community Survey (2011-2016) to estimate effects on coverage among nonelderly adults with incomes up to 138% of the federal poverty level. METHODS: We estimated differences in differences (DID) in marginal probabilities following probit models, comparing New York, Vermont, Massachusetts, and Delaware with nonexpansion states on the East Coast. RESULTS: There is strong evidence of the effect in New York: DID estimates ranged from 3.3 to 5.2 percentage points. There is weak or no evidence of coverage gains in the other 3 states. Pronounced effects were found among the racial/ethnic majority (white, non-Hispanic white, and nonblack populations) in New York, as well as the working poor and previously eligible in New York and Massachusetts. CONCLUSIONS: Even in states with relatively generous pre-expansion Medicaid programs, the expansion can produce nontrivial coverage gains, as evidenced by New York. Our findings of spillover effects may indicate the relative importance and success of a simplified enrollment process and increased media coverage in boosting enrollment in Medicaid. Our subgroup analyses highlight a potential need to improve access to office-based care to accommodate the growing population of the working poor on Medicaid and potential changes in the Medicaid risk pool served by managed care organizations and subsequent decreases in capitated payments.